Advances in Computational Modeling of Traumatic Brain Injury: Emerging Trends and Future Perspectives

Traumatic brain injury (TBI) remains a major global health concern, contributing substantially to neurological disability, mortality, and long-term cognitive and motor impairments. The intricate biomechanical response of the brain under external loading makes it challenging to determine how mechanical forces lead to localized tissue damage. Computational modeling has emerged as a powerful approach to explore these mechanisms, offering quantitative insight into stress, strain, and deformation patterns that are difficult to measure experimentally. Over the past decade, significant progress in numerical techniques, particularly finite element analysis (FEA), has enabled high-fidelity simulations incorporating the nonlinear, anisotropic, and viscoelastic characteristics of brain tissues.Recent advancements in fluid–structure interaction (FSI) modeling, multiscale frameworks, and multiphysics coupling have further improved the realism of brain injury simulations by capturing interactions among mechanical, fluidic, and physiological processes. Integration of neuroimaging data such as MRI and CT has facilitated the development of subject-specific and pathology-informed models, while machine learning and data-driven optimization have expanded opportunities for model calibration, parameter estimation, and predictive injury assessment. These approaches not only enhance model fidelity but also support the transition toward personalized and precision-based neurotrauma research.Despite these advances, challenges persist in model validation, standardization of constitutive parameters, and cross-scale correlation between mechanical metrics and biological injury markers. Addressing these limitations requires coordinated interdisciplinary collaboration and the establishment of benchmark datasets for model verification. This study provides a comprehensive overview of recent developments, emerging methodologies, and future perspectives in computational modeling of TBI, highlighting its growing potential for improving diagnostic accuracy, preventive design, and individualized therapeutic strategies in brain injury research.

کلیدواژه ها:

Traumatic Brain Injury (TBI) ، Computational Modeling ، Finite Element Analysis (FEA) ، Biomechanics ، Machine Learning